Clustering and machine learning techniques identify air pollution regimes in Greater Cairo.

This project framework provides implementation support and guidance for analyzing urban air quality using unsupervised machine learning and clustering techniques. Focusing on the identification of distinct air pollution regimes within dense urban environments, specifically modeled after the Greater Cairo region, the methodology utilizes historical air quality index (AQI) datasets containing particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The proposed system guides students through data preprocessing, feature scaling, and the application of clustering algorithms such as K-Means, Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to categorize spatial and temporal pollution patterns. Additionally, classification models like Random Forest or Support Vector

Machines (SVM) are integrated to validate the identified regimes and predict pollution levels based on meteorological features. This structured approach assists B.Tech students in understanding the correlation between urban activities, meteorological factors, and localized pollution hotspots. The project documentation supports the development of a localized air quality monitoring dashboard, offering practical insights into environmental data science and assisting in the formulation of targeted urban emission control strategies.

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